Connected and Autonomous Vehicles (CAVs) are typically equipped with multiple advanced on-board sensors generating a massive amount of data. Utilizing and processing such data to improve the performance of CAVs is a current research area. Machine learning techniques are effective ways of exploiting such data in many applications with many demonstrated success stories. In this chapter, first, we provide an overview of recent advances in applying machine learning in the emerging area of CAVs including particular applications and highlight several open issues in the area. Second, as a case study and a particular application, we present a novel deep learning approach to control the steering angle for cooperative self-driving cars capable of integrating both local and remote information. In that application, we tackle the problem of utilizing multiple sets of images shared between two autonomous vehicles to improve the accuracy of controlling the steering angle by considering the temporal dependencies between the image frames. This problem has not been studied in the literature widely. We present and study a new deep architecture to predict the steering angle automatically. Our deep architecture is an end-to-end network that utilizes Convolutional-Neural- Networks (CNN), Long-Short-Term-Memory (LSTM) and fully connected (FC) layers; it processes both present and future images (shared by a vehicle ahead via Vehicle-to-Vehicle (V2V) communication) as input to control the steering angle. In our simulations, we demonstrate that using a combination of perception and communication systems can improve robustness and safety of CAVs. Our model demonstrates the lowest error when compared to the other existing approaches in the literature.